Instructions to use vblagoje/bert-english-uncased-finetuned-pos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vblagoje/bert-english-uncased-finetuned-pos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vblagoje/bert-english-uncased-finetuned-pos")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vblagoje/bert-english-uncased-finetuned-pos") model = AutoModelForTokenClassification.from_pretrained("vblagoje/bert-english-uncased-finetuned-pos") - Inference
- Notebooks
- Google Colab
- Kaggle
Create README.md (#6)
Browse files- Create README.md (4467309ca73fd9376c9c6c216903b2ce0bae2116)
Co-authored-by: Carlos Agamez <CarlosAGDev@users.noreply.huggingface.co>
README.md
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## Part-of-Speech (PoS) Tags
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Below are the Part-of-Speech (PoS) tags used in the model:
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| **Tag** | **Meaning** | **Examples** |
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|-----------|------------------------------------------------------|--------------------------------|
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| ADP | Adposition (prepositions or postpositions) | in, on, by |
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| ADJ | Adjective | significant, global |
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| ADV | Adverb | quickly, often |
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| AUX | Auxiliary verb | is, was |
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| CCONJ | Coordinating conjunction | and, but |
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| DET | Determiner | the, a |
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| INTJ | Interjection | oh, wow |
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| NOUN | Noun | man, city |
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| NUM | Number | one, 2022 |
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| PART | Particle | 's, to |
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| PRON | Pronoun | he, which |
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| PROPN | Proper noun | Neil Armstrong, Paris |
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| PUNCT | Punctuation mark | ,, . |
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| SCONJ | Subordinating conjunction | because, although |
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| SYM | Symbol | $, % |
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| VERB | Verb | run, is |
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| X | Other (generally words that do not fit into other categories) | [not defined] |
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